Overview

Dataset statistics

Number of variables33
Number of observations10000
Missing cells167011
Missing cells (%)50.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 MiB
Average record size in memory296.0 B

Variable types

Numeric9
Text4
Categorical2
Boolean2
DateTime2
Unsupported14

Dataset

Description기초안전보건교육 실시계획 정보
Author한국산업안전보건공단
URLhttps://www.data.go.kr/data/15066093/fileData.do

Alerts

DEL_YN has constant value ""Constant
LCTRUM_AR is highly imbalanced (61.2%)Imbalance
PROCESS_STTUS is highly imbalanced (97.8%)Imbalance
UNIQ_ID has 8733 (87.3%) missing valuesMissing
LCTRUM_ZIP has 9205 (92.0%) missing valuesMissing
LCTRUM_ADRES2 has 443 (4.4%) missing valuesMissing
MNG_LABOFFICE has 558 (5.6%) missing valuesMissing
MNG_AGENT has 558 (5.6%) missing valuesMissing
FRST_REGISTER_PNTTM has 473 (4.7%) missing valuesMissing
LAST_UPDUSR_PNTTM has 7041 (70.4%) missing valuesMissing
FILE_STRE_COURS1 has 10000 (100.0%) missing valuesMissing
STRE_FILE_NM1 has 10000 (100.0%) missing valuesMissing
ORIGNL_FILE_NM1 has 10000 (100.0%) missing valuesMissing
FILE_STRE_COURS2 has 10000 (100.0%) missing valuesMissing
STRE_FILE_NM2 has 10000 (100.0%) missing valuesMissing
ORIGNL_FILE_NM2 has 10000 (100.0%) missing valuesMissing
FILE_STRE_COURS3 has 10000 (100.0%) missing valuesMissing
STRE_FILE_NM3 has 10000 (100.0%) missing valuesMissing
ORIGNL_FILE_NM3 has 10000 (100.0%) missing valuesMissing
FILE_STRE_COURS4 has 10000 (100.0%) missing valuesMissing
STRE_FILE_NM4 has 10000 (100.0%) missing valuesMissing
ORIGNL_FILE_NM4 has 10000 (100.0%) missing valuesMissing
PYMNT_DCSN_DE has 10000 (100.0%) missing valuesMissing
RCOGN_NMPR has 10000 (100.0%) missing valuesMissing
EDC_YEAR is highly skewed (γ1 = 48.39583084)Skewed
EDC_DT is highly skewed (γ1 = 48.42075221)Skewed
FILE_STRE_COURS1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
STRE_FILE_NM1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
ORIGNL_FILE_NM1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
FILE_STRE_COURS2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
STRE_FILE_NM2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
ORIGNL_FILE_NM2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
FILE_STRE_COURS3 is an unsupported type, check if it needs cleaning or further analysisUnsupported
STRE_FILE_NM3 is an unsupported type, check if it needs cleaning or further analysisUnsupported
ORIGNL_FILE_NM3 is an unsupported type, check if it needs cleaning or further analysisUnsupported
FILE_STRE_COURS4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
STRE_FILE_NM4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
ORIGNL_FILE_NM4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
PYMNT_DCSN_DE is an unsupported type, check if it needs cleaning or further analysisUnsupported
RCOGN_NMPR is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 08:53:01.289324
Analysis finished2023-12-12 08:53:02.827354
Duration1.54 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

EDC_YEAR
Real number (ℝ)

SKEWED 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.3657
Minimum2009
Maximum2414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:53:02.891881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2010
Q12013
median2014
Q32014
95-th percentile2014
Maximum2414
Range405
Interquartile range (IQR)1

Descriptive statistics

Standard deviation8.1006497
Coefficient of variation (CV)0.0040234368
Kurtosis2391.8465
Mean2013.3657
Median Absolute Deviation (MAD)1
Skewness48.395831
Sum20133657
Variance65.620526
MonotonicityNot monotonic
2023-12-12T17:53:03.047382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2014 4919
49.2%
2013 3288
32.9%
2012 1092
 
10.9%
2009 397
 
4.0%
2010 142
 
1.4%
2015 139
 
1.4%
2011 19
 
0.2%
2414 4
 
< 0.1%
ValueCountFrequency (%)
2009 397
 
4.0%
2010 142
 
1.4%
2011 19
 
0.2%
2012 1092
 
10.9%
2013 3288
32.9%
2014 4919
49.2%
2015 139
 
1.4%
2414 4
 
< 0.1%
ValueCountFrequency (%)
2414 4
 
< 0.1%
2015 139
 
1.4%
2014 4919
49.2%
2013 3288
32.9%
2012 1092
 
10.9%
2011 19
 
0.2%
2010 142
 
1.4%
2009 397
 
4.0%

UNIQ_ID
Real number (ℝ)

MISSING 

Distinct1267
Distinct (%)100.0%
Missing8733
Missing (%)87.3%
Infinite0
Infinite (%)0.0%
Mean17586.539
Minimum13
Maximum43496
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:53:03.228833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile596.4
Q13787.5
median16114
Q329536
95-th percentile41053.7
Maximum43496
Range43483
Interquartile range (IQR)25748.5

Descriptive statistics

Standard deviation13935.751
Coefficient of variation (CV)0.79241012
Kurtosis-1.234343
Mean17586.539
Median Absolute Deviation (MAD)12429
Skewness0.3590532
Sum22282145
Variance1.9420517 × 108
MonotonicityNot monotonic
2023-12-12T17:53:03.369845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21186 1
 
< 0.1%
7732 1
 
< 0.1%
34255 1
 
< 0.1%
4123 1
 
< 0.1%
27512 1
 
< 0.1%
33996 1
 
< 0.1%
17267 1
 
< 0.1%
2299 1
 
< 0.1%
3595 1
 
< 0.1%
3458 1
 
< 0.1%
Other values (1257) 1257
 
12.6%
(Missing) 8733
87.3%
ValueCountFrequency (%)
13 1
< 0.1%
18 1
< 0.1%
20 1
< 0.1%
27 1
< 0.1%
37 1
< 0.1%
39 1
< 0.1%
63 1
< 0.1%
65 1
< 0.1%
70 1
< 0.1%
73 1
< 0.1%
ValueCountFrequency (%)
43496 1
< 0.1%
43453 1
< 0.1%
43382 1
< 0.1%
43378 1
< 0.1%
43352 1
< 0.1%
43291 1
< 0.1%
43283 1
< 0.1%
43281 1
< 0.1%
43270 1
< 0.1%
43235 1
< 0.1%

EDC_DT
Real number (ℝ)

SKEWED 

Distinct1097
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20134392
Minimum20090714
Maximum24141212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:53:03.518469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090714
5-th percentile20101123
Q120130418
median20140108
Q320140723
95-th percentile20141205
Maximum24141212
Range4050498
Interquartile range (IQR)10305.25

Descriptive statistics

Standard deviation81002.295
Coefficient of variation (CV)0.0040230813
Kurtosis2393.4802
Mean20134392
Median Absolute Deviation (MAD)8887
Skewness48.420752
Sum2.0134392 × 1011
Variance6.5613718 × 109
MonotonicityNot monotonic
2023-12-12T17:53:03.670599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20141013 32
 
0.3%
20141114 32
 
0.3%
20141007 32
 
0.3%
20141107 30
 
0.3%
20141202 30
 
0.3%
20141209 28
 
0.3%
20141021 28
 
0.3%
20140822 28
 
0.3%
20140813 27
 
0.3%
20141104 27
 
0.3%
Other values (1087) 9706
97.1%
ValueCountFrequency (%)
20090714 1
 
< 0.1%
20090718 2
< 0.1%
20090720 1
 
< 0.1%
20090721 3
< 0.1%
20090723 2
< 0.1%
20090724 1
 
< 0.1%
20090725 1
 
< 0.1%
20090728 1
 
< 0.1%
20090729 2
< 0.1%
20090730 3
< 0.1%
ValueCountFrequency (%)
24141212 2
< 0.1%
24141208 2
< 0.1%
20150331 1
< 0.1%
20150327 1
< 0.1%
20150326 1
< 0.1%
20150324 1
< 0.1%
20150320 1
< 0.1%
20150316 2
< 0.1%
20150311 1
< 0.1%
20150306 1
< 0.1%

EDC_TIME_S
Real number (ℝ)

Distinct37
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1102.449
Minimum600
Maximum1900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:53:03.814772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum600
5-th percentile800
Q1800
median1300
Q31300
95-th percentile1400
Maximum1900
Range1300
Interquartile range (IQR)500

Descriptive statistics

Standard deviation263.09679
Coefficient of variation (CV)0.23864758
Kurtosis-1.4448171
Mean1102.449
Median Absolute Deviation (MAD)100
Skewness-0.021136498
Sum11024490
Variance69219.919
MonotonicityNot monotonic
2023-12-12T17:53:03.938567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1300 3238
32.4%
800 2320
23.2%
1330 1106
 
11.1%
830 1088
 
10.9%
1400 931
 
9.3%
900 841
 
8.4%
730 209
 
2.1%
1800 49
 
0.5%
700 33
 
0.3%
1700 25
 
0.2%
Other values (27) 160
 
1.6%
ValueCountFrequency (%)
600 1
 
< 0.1%
700 33
 
0.3%
720 1
 
< 0.1%
730 209
 
2.1%
740 1
 
< 0.1%
750 4
 
< 0.1%
800 2320
23.2%
810 2
 
< 0.1%
815 1
 
< 0.1%
820 1
 
< 0.1%
ValueCountFrequency (%)
1900 8
 
0.1%
1830 18
 
0.2%
1810 1
 
< 0.1%
1800 49
0.5%
1730 19
 
0.2%
1710 1
 
< 0.1%
1700 25
0.2%
1600 5
 
0.1%
1530 5
 
0.1%
1500 16
 
0.2%

EDC_TIME_E
Real number (ℝ)

Distinct37
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1502.429
Minimum1000
Maximum2300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:53:04.085962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1200
Q11200
median1700
Q31700
95-th percentile1800
Maximum2300
Range1300
Interquartile range (IQR)500

Descriptive statistics

Standard deviation263.08177
Coefficient of variation (CV)0.17510429
Kurtosis-1.44463
Mean1502.429
Median Absolute Deviation (MAD)100
Skewness-0.021051632
Sum15024290
Variance69212.016
MonotonicityNot monotonic
2023-12-12T17:53:04.249762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1700 3238
32.4%
1200 2320
23.2%
1730 1106
 
11.1%
1230 1088
 
10.9%
1800 930
 
9.3%
1300 841
 
8.4%
1130 209
 
2.1%
2200 49
 
0.5%
1100 33
 
0.3%
2100 25
 
0.2%
Other values (27) 161
 
1.6%
ValueCountFrequency (%)
1000 1
 
< 0.1%
1100 33
 
0.3%
1120 1
 
< 0.1%
1130 209
 
2.1%
1140 1
 
< 0.1%
1150 4
 
< 0.1%
1200 2320
23.2%
1210 2
 
< 0.1%
1215 1
 
< 0.1%
1220 1
 
< 0.1%
ValueCountFrequency (%)
2300 8
 
0.1%
2230 18
 
0.2%
2210 1
 
< 0.1%
2200 49
0.5%
2130 19
 
0.2%
2110 1
 
< 0.1%
2100 25
0.2%
2000 5
 
0.1%
1930 5
 
0.1%
1900 16
 
0.2%

EDC_SBJECT
Text

Distinct120
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T17:53:04.458749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length13.7012
Min length6

Characters and Unicode

Total characters137012
Distinct characters78
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)0.5%

Sample

1st row건설안전및근로자건강관리
2nd row건설안전 및 보건교육
3rd row건설안전 및 보건위생
4th row건설안전 및 근로자 건강관리
5th row건설안전 및 근로자 건강관리
ValueCountFrequency (%)
및 7607
23.8%
건설안전 7477
23.4%
건강관리 5442
17.0%
근로자 5238
16.4%
근로자건강관리 1448
 
4.5%
기초안전보건교육 1127
 
3.5%
건설업 914
 
2.9%
보건위생 561
 
1.8%
건설업기초안전보건교육 417
 
1.3%
건설안전및근로자건강관리 367
 
1.1%
Other values (93) 1379
 
4.3%
2023-12-12T17:53:04.765452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22024
16.1%
건 19494
14.2%
안 9911
 
7.2%
전 9911
 
7.2%
설 9618
 
7.0%
및 8242
 
6.0%
강 7380
 
5.4%
관 7376
 
5.4%
리 7375
 
5.4%
로 7085
 
5.2%
Other values (68) 28596
20.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 114858
83.8%
Space Separator 22024
 
16.1%
Open Punctuation 46
 
< 0.1%
Close Punctuation 46
 
< 0.1%
Decimal Number 24
 
< 0.1%
Other Punctuation 6
 
< 0.1%
Control 6
 
< 0.1%
Math Symbol 1
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
건 19494
17.0%
안 9911
8.6%
전 9911
8.6%
설 9618
8.4%
및 8242
 
7.2%
강 7380
 
6.4%
관 7376
 
6.4%
리 7375
 
6.4%
로 7085
 
6.2%
근 7085
 
6.2%
Other values (58) 21381
18.6%
Decimal Number
ValueCountFrequency (%)
2 11
45.8%
1 9
37.5%
3 4
 
16.7%
Space Separator
ValueCountFrequency (%)
22024
100.0%
Open Punctuation
ValueCountFrequency (%)
( 46
100.0%
Close Punctuation
ValueCountFrequency (%)
) 46
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%
Control
ValueCountFrequency (%)
6
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 114858
83.8%
Common 22154
 
16.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
건 19494
17.0%
안 9911
8.6%
전 9911
8.6%
설 9618
8.4%
및 8242
 
7.2%
강 7380
 
6.4%
관 7376
 
6.4%
리 7375
 
6.4%
로 7085
 
6.2%
근 7085
 
6.2%
Other values (58) 21381
18.6%
Common
ValueCountFrequency (%)
22024
99.4%
( 46
 
0.2%
) 46
 
0.2%
2 11
 
< 0.1%
1 9
 
< 0.1%
, 6
 
< 0.1%
6
 
< 0.1%
3 4
 
< 0.1%
+ 1
 
< 0.1%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 114858
83.8%
ASCII 22154
 
16.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22024
99.4%
( 46
 
0.2%
) 46
 
0.2%
2 11
 
< 0.1%
1 9
 
< 0.1%
, 6
 
< 0.1%
6
 
< 0.1%
3 4
 
< 0.1%
+ 1
 
< 0.1%
- 1
 
< 0.1%
Hangul
ValueCountFrequency (%)
건 19494
17.0%
안 9911
8.6%
전 9911
8.6%
설 9618
8.4%
및 8242
 
7.2%
강 7380
 
6.4%
관 7376
 
6.4%
리 7375
 
6.4%
로 7085
 
6.2%
근 7085
 
6.2%
Other values (58) 21381
18.6%

EDC_CNT
Real number (ℝ)

Distinct39
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.9234
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:53:04.914395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30
Q150
median50
Q350
95-th percentile50
Maximum100
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.2490689
Coefficient of variation (CV)0.17213029
Kurtosis9.2358424
Mean47.9234
Median Absolute Deviation (MAD)0
Skewness-1.1978276
Sum479234
Variance68.047137
MonotonicityNot monotonic
2023-12-12T17:53:05.133285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
50 8581
85.8%
30 449
 
4.5%
20 374
 
3.7%
40 331
 
3.3%
70 85
 
0.9%
60 52
 
0.5%
100 25
 
0.2%
45 16
 
0.2%
80 16
 
0.2%
15 9
 
0.1%
Other values (29) 62
 
0.6%
ValueCountFrequency (%)
1 1
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 9
 
0.1%
12 2
 
< 0.1%
15 9
 
0.1%
18 3
 
< 0.1%
20 374
3.7%
25 6
 
0.1%
ValueCountFrequency (%)
100 25
 
0.2%
98 1
 
< 0.1%
90 3
 
< 0.1%
80 16
 
0.2%
78 1
 
< 0.1%
75 2
 
< 0.1%
70 85
0.9%
68 1
 
< 0.1%
65 4
 
< 0.1%
60 52
0.5%

LCTRUM_AR
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
40
8495 
<NA>
 
584
20
 
362
10
 
327
30
 
232

Length

Max length4
Median length2
Mean length2.1168
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40
2nd row40
3rd row40
4th row40
5th row40

Common Values

ValueCountFrequency (%)
40 8495
85.0%
<NA> 584
 
5.8%
20 362
 
3.6%
10 327
 
3.3%
30 232
 
2.3%

Length

2023-12-12T17:53:05.324478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:53:05.476684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
40 8495
85.0%
na 584
 
5.8%
20 362
 
3.6%
10 327
 
3.3%
30 232
 
2.3%

EDC_PLACE
Text

Distinct2198
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T17:53:05.867898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length38
Mean length8.5068
Min length2

Characters and Unicode

Total characters85068
Distinct characters548
Distinct categories14 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1728 ?
Unique (%)17.3%

Sample

1st row강의실1
2nd row강의실1
3rd rowKB건설안전연구원
4th row강서구 재건축 공사 현장
5th row강의실1
ValueCountFrequency (%)
강의실1 3361
 
21.2%
현장 579
 
3.6%
교육장 552
 
3.5%
등록강의실 546
 
3.4%
강의실 432
 
2.7%
동화안전기술원 262
 
1.6%
강의실2 228
 
1.4%
신축공사 205
 
1.3%
본강의장 198
 
1.2%
아파트 178
 
1.1%
Other values (3036) 9348
58.8%
2023-12-12T17:53:06.481114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5955
 
7.0%
강 5507
 
6.5%
의 5409
 
6.4%
실 5227
 
6.1%
1 4094
 
4.8%
장 3162
 
3.7%
교 1858
 
2.2%
전 1781
 
2.1%
육 1729
 
2.0%
건 1661
 
2.0%
Other values (538) 48685
57.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66711
78.4%
Space Separator 5955
 
7.0%
Decimal Number 5902
 
6.9%
Uppercase Letter 2977
 
3.5%
Lowercase Letter 934
 
1.1%
Close Punctuation 855
 
1.0%
Open Punctuation 849
 
1.0%
Dash Punctuation 494
 
0.6%
Other Punctuation 260
 
0.3%
Other Symbol 61
 
0.1%
Other values (4) 70
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
강 5507
 
8.3%
의 5409
 
8.1%
실 5227
 
7.8%
장 3162
 
4.7%
교 1858
 
2.8%
전 1781
 
2.7%
육 1729
 
2.6%
건 1661
 
2.5%
설 1615
 
2.4%
안 1476
 
2.2%
Other values (462) 37286
55.9%
Uppercase Letter
ValueCountFrequency (%)
B 344
11.6%
S 304
 
10.2%
K 267
 
9.0%
R 214
 
7.2%
P 195
 
6.6%
L 182
 
6.1%
T 181
 
6.1%
C 158
 
5.3%
D 147
 
4.9%
A 141
 
4.7%
Other values (15) 844
28.4%
Lowercase Letter
ValueCountFrequency (%)
e 171
18.3%
o 92
9.9%
r 82
8.8%
t 80
8.6%
c 73
7.8%
p 64
 
6.9%
a 63
 
6.7%
j 63
 
6.7%
n 62
 
6.6%
s 33
 
3.5%
Other values (13) 151
16.2%
Decimal Number
ValueCountFrequency (%)
1 4094
69.4%
2 742
 
12.6%
3 241
 
4.1%
5 240
 
4.1%
4 190
 
3.2%
6 156
 
2.6%
0 79
 
1.3%
7 71
 
1.2%
8 54
 
0.9%
9 35
 
0.6%
Other Punctuation
ValueCountFrequency (%)
, 164
63.1%
/ 39
 
15.0%
. 31
 
11.9%
# 14
 
5.4%
& 7
 
2.7%
; 3
 
1.2%
' 1
 
0.4%
· 1
 
0.4%
Control
ValueCountFrequency (%)
9
90.0%
1
 
10.0%
Space Separator
ValueCountFrequency (%)
5955
100.0%
Close Punctuation
ValueCountFrequency (%)
) 855
100.0%
Open Punctuation
ValueCountFrequency (%)
( 849
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 494
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 61
100.0%
Math Symbol
ValueCountFrequency (%)
~ 47
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 9
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66772
78.5%
Common 14381
 
16.9%
Latin 3915
 
4.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
강 5507
 
8.2%
의 5409
 
8.1%
실 5227
 
7.8%
장 3162
 
4.7%
교 1858
 
2.8%
전 1781
 
2.7%
육 1729
 
2.6%
건 1661
 
2.5%
설 1615
 
2.4%
안 1476
 
2.2%
Other values (463) 37347
55.9%
Latin
ValueCountFrequency (%)
B 344
 
8.8%
S 304
 
7.8%
K 267
 
6.8%
R 214
 
5.5%
P 195
 
5.0%
L 182
 
4.6%
T 181
 
4.6%
e 171
 
4.4%
C 158
 
4.0%
D 147
 
3.8%
Other values (39) 1752
44.8%
Common
ValueCountFrequency (%)
5955
41.4%
1 4094
28.5%
) 855
 
5.9%
( 849
 
5.9%
2 742
 
5.2%
- 494
 
3.4%
3 241
 
1.7%
5 240
 
1.7%
4 190
 
1.3%
, 164
 
1.1%
Other values (16) 557
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66710
78.4%
ASCII 18291
 
21.5%
None 62
 
0.1%
Number Forms 4
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5955
32.6%
1 4094
22.4%
) 855
 
4.7%
( 849
 
4.6%
2 742
 
4.1%
- 494
 
2.7%
B 344
 
1.9%
S 304
 
1.7%
K 267
 
1.5%
3 241
 
1.3%
Other values (63) 4146
22.7%
Hangul
ValueCountFrequency (%)
강 5507
 
8.3%
의 5409
 
8.1%
실 5227
 
7.8%
장 3162
 
4.7%
교 1858
 
2.8%
전 1781
 
2.7%
육 1729
 
2.6%
건 1661
 
2.5%
설 1615
 
2.4%
안 1476
 
2.2%
Other values (461) 37285
55.9%
None
ValueCountFrequency (%)
㈜ 61
98.4%
· 1
 
1.6%
Number Forms
ValueCountFrequency (%)
Ⅱ 4
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅅ 1
100.0%

EDC_TYPE
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
10
6764 
20
2674 
<NA>
 
562

Length

Max length4
Median length2
Mean length2.1124
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row20
5th row10

Common Values

ValueCountFrequency (%)
10 6764
67.6%
20 2674
 
26.7%
<NA> 562
 
5.6%

Length

2023-12-12T17:53:06.633894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:53:06.752116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10 6764
67.6%
20 2674
 
26.7%
na 562
 
5.6%

LCTRUM_ZIP
Real number (ℝ)

MISSING 

Distinct439
Distinct (%)55.2%
Missing9205
Missing (%)92.0%
Infinite0
Infinite (%)0.0%
Mean441711.78
Minimum0
Maximum791944
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:53:06.892825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile133796.6
Q1339811.5
median443270
Q3607925
95-th percentile741855.6
Maximum791944
Range791944
Interquartile range (IQR)268113.5

Descriptive statistics

Standard deviation187907.29
Coefficient of variation (CV)0.42540701
Kurtosis-0.75514287
Mean441711.78
Median Absolute Deviation (MAD)122316
Skewness-0.16781649
Sum3.5116086 × 108
Variance3.5309148 × 1010
MonotonicityNot monotonic
2023-12-12T17:53:07.066588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
406840 34
 
0.3%
430016 18
 
0.2%
461200 13
 
0.1%
336841 13
 
0.1%
152050 10
 
0.1%
613100 9
 
0.1%
540978 9
 
0.1%
138170 9
 
0.1%
445810 9
 
0.1%
619952 9
 
0.1%
Other values (429) 662
 
6.6%
(Missing) 9205
92.0%
ValueCountFrequency (%)
0 1
< 0.1%
100051 1
< 0.1%
100052 1
< 0.1%
100101 1
< 0.1%
100192 1
< 0.1%
100400 1
< 0.1%
100440 1
< 0.1%
100450 2
< 0.1%
100802 1
< 0.1%
110062 1
< 0.1%
ValueCountFrequency (%)
791944 1
 
< 0.1%
791052 1
 
< 0.1%
791050 6
0.1%
790827 1
 
< 0.1%
790785 4
< 0.1%
790704 1
 
< 0.1%
790360 5
0.1%
790150 1
 
< 0.1%
780900 1
 
< 0.1%
780892 1
 
< 0.1%

LCTRUM_ADRES1
Text

Distinct1479
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T17:53:07.473170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length35
Mean length11.6224
Min length5

Characters and Unicode

Total characters116224
Distinct characters364
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique831 ?
Unique (%)8.3%

Sample

1st row부산 동구 범일2동
2nd row인천 계양구 작전동
3rd row경기 오산시 외삼미동
4th row서울 강서구
5th row경기 수원시 장안구 영화동
ValueCountFrequency (%)
경기 2666
 
8.1%
서울 1968
 
6.0%
화성시 706
 
2.1%
부산 641
 
1.9%
북구 571
 
1.7%
충남 561
 
1.7%
인천 554
 
1.7%
대구 483
 
1.5%
수원시 461
 
1.4%
경남 415
 
1.3%
Other values (1622) 24003
72.7%
2023-12-12T17:53:07.998088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24030
20.7%
동 9114
 
7.8%
구 7396
 
6.4%
시 5119
 
4.4%
경 3714
 
3.2%
기 3177
 
2.7%
서 2975
 
2.6%
남 2356
 
2.0%
산 2355
 
2.0%
울 2331
 
2.0%
Other values (354) 53657
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89090
76.7%
Space Separator 24030
 
20.7%
Decimal Number 2852
 
2.5%
Dash Punctuation 209
 
0.2%
Uppercase Letter 28
 
< 0.1%
Open Punctuation 4
 
< 0.1%
Close Punctuation 4
 
< 0.1%
Lowercase Letter 3
 
< 0.1%
Other Punctuation 2
 
< 0.1%
Control 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 9114
 
10.2%
구 7396
 
8.3%
시 5119
 
5.7%
경 3714
 
4.2%
기 3177
 
3.6%
서 2975
 
3.3%
남 2356
 
2.6%
산 2355
 
2.6%
울 2331
 
2.6%
천 1685
 
1.9%
Other values (324) 48868
54.9%
Uppercase Letter
ValueCountFrequency (%)
A 7
25.0%
B 4
14.3%
C 3
10.7%
L 3
10.7%
M 3
10.7%
E 2
 
7.1%
T 2
 
7.1%
R 1
 
3.6%
W 1
 
3.6%
O 1
 
3.6%
Decimal Number
ValueCountFrequency (%)
1 691
24.2%
3 618
21.7%
2 507
17.8%
4 245
 
8.6%
5 186
 
6.5%
7 176
 
6.2%
6 116
 
4.1%
9 108
 
3.8%
0 108
 
3.8%
8 97
 
3.4%
Lowercase Letter
ValueCountFrequency (%)
b 1
33.3%
k 1
33.3%
o 1
33.3%
Space Separator
ValueCountFrequency (%)
24030
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 209
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89090
76.7%
Common 27103
 
23.3%
Latin 31
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 9114
 
10.2%
구 7396
 
8.3%
시 5119
 
5.7%
경 3714
 
4.2%
기 3177
 
3.6%
서 2975
 
3.3%
남 2356
 
2.6%
산 2355
 
2.6%
울 2331
 
2.6%
천 1685
 
1.9%
Other values (324) 48868
54.9%
Common
ValueCountFrequency (%)
24030
88.7%
1 691
 
2.5%
3 618
 
2.3%
2 507
 
1.9%
4 245
 
0.9%
- 209
 
0.8%
5 186
 
0.7%
7 176
 
0.6%
6 116
 
0.4%
9 108
 
0.4%
Other values (6) 217
 
0.8%
Latin
ValueCountFrequency (%)
A 7
22.6%
B 4
12.9%
C 3
9.7%
L 3
9.7%
M 3
9.7%
E 2
 
6.5%
T 2
 
6.5%
b 1
 
3.2%
k 1
 
3.2%
R 1
 
3.2%
Other values (4) 4
12.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89090
76.7%
ASCII 27134
 
23.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24030
88.6%
1 691
 
2.5%
3 618
 
2.3%
2 507
 
1.9%
4 245
 
0.9%
- 209
 
0.8%
5 186
 
0.7%
7 176
 
0.6%
6 116
 
0.4%
9 108
 
0.4%
Other values (20) 248
 
0.9%
Hangul
ValueCountFrequency (%)
동 9114
 
10.2%
구 7396
 
8.3%
시 5119
 
5.7%
경 3714
 
4.2%
기 3177
 
3.6%
서 2975
 
3.3%
남 2356
 
2.6%
산 2355
 
2.6%
울 2331
 
2.6%
천 1685
 
1.9%
Other values (324) 48868
54.9%

LCTRUM_ADRES2
Text

MISSING 

Distinct1894
Distinct (%)19.8%
Missing443
Missing (%)4.4%
Memory size156.2 KiB
2023-12-12T17:53:08.367387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length34
Mean length10.532385
Min length1

Characters and Unicode

Total characters100658
Distinct characters395
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1278 ?
Unique (%)13.4%

Sample

1st row830-51
2nd row853-10(5층)
3rd row53번지 강남빌딩 201호
4th row화곡동
5th row392-4 칠공빌딩 3층
ValueCountFrequency (%)
4층 872
 
5.1%
2층 575
 
3.3%
3층 559
 
3.2%
7층 238
 
1.4%
694-9(401호 216
 
1.3%
41-5(동탄성심플라자 190
 
1.1%
54-54 163
 
0.9%
중앙빌딩(5층 161
 
0.9%
201호 157
 
0.9%
137(피카디리플러스 153
 
0.9%
Other values (2348) 13982
81.0%
2023-12-12T17:53:09.022511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 8252
 
8.2%
7981
 
7.9%
- 6435
 
6.4%
4 5909
 
5.9%
3 5668
 
5.6%
2 5594
 
5.6%
5 4837
 
4.8%
0 4151
 
4.1%
층 3927
 
3.9%
( 3858
 
3.8%
Other values (385) 44046
43.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45133
44.8%
Other Letter 32182
32.0%
Space Separator 7981
 
7.9%
Dash Punctuation 6435
 
6.4%
Open Punctuation 3859
 
3.8%
Close Punctuation 3853
 
3.8%
Uppercase Letter 476
 
0.5%
Other Punctuation 458
 
0.5%
Math Symbol 231
 
0.2%
Lowercase Letter 43
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
층 3927
 
12.2%
지 1978
 
6.1%
번 1577
 
4.9%
리 1391
 
4.3%
호 1283
 
4.0%
빌 1117
 
3.5%
딩 1080
 
3.4%
동 713
 
2.2%
자 626
 
1.9%
라 607
 
1.9%
Other values (327) 17883
55.6%
Uppercase Letter
ValueCountFrequency (%)
A 98
20.6%
B 97
20.4%
L 83
17.4%
M 74
15.5%
J 59
12.4%
C 10
 
2.1%
G 8
 
1.7%
D 7
 
1.5%
K 6
 
1.3%
R 5
 
1.1%
Other values (11) 29
 
6.1%
Lowercase Letter
ValueCountFrequency (%)
m 29
67.4%
b 3
 
7.0%
a 3
 
7.0%
r 1
 
2.3%
e 1
 
2.3%
h 1
 
2.3%
p 1
 
2.3%
u 1
 
2.3%
y 1
 
2.3%
l 1
 
2.3%
Decimal Number
ValueCountFrequency (%)
1 8252
18.3%
4 5909
13.1%
3 5668
12.6%
2 5594
12.4%
5 4837
10.7%
0 4151
9.2%
7 3477
7.7%
6 2624
 
5.8%
9 2561
 
5.7%
8 2060
 
4.6%
Other Punctuation
ValueCountFrequency (%)
, 432
94.3%
/ 18
 
3.9%
. 6
 
1.3%
: 1
 
0.2%
\ 1
 
0.2%
Math Symbol
ValueCountFrequency (%)
> 103
44.6%
∼ 74
32.0%
~ 53
22.9%
= 1
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 3858
> 99.9%
[ 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 3852
> 99.9%
] 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
7981
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6435
100.0%
Control
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 67957
67.5%
Hangul 32181
32.0%
Latin 519
 
0.5%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
층 3927
 
12.2%
지 1978
 
6.1%
번 1577
 
4.9%
리 1391
 
4.3%
호 1283
 
4.0%
빌 1117
 
3.5%
딩 1080
 
3.4%
동 713
 
2.2%
자 626
 
1.9%
라 607
 
1.9%
Other values (326) 17882
55.6%
Latin
ValueCountFrequency (%)
A 98
18.9%
B 97
18.7%
L 83
16.0%
M 74
14.3%
J 59
11.4%
m 29
 
5.6%
C 10
 
1.9%
G 8
 
1.5%
D 7
 
1.3%
K 6
 
1.2%
Other values (22) 48
9.2%
Common
ValueCountFrequency (%)
1 8252
12.1%
7981
11.7%
- 6435
9.5%
4 5909
8.7%
3 5668
8.3%
2 5594
8.2%
5 4837
7.1%
0 4151
 
6.1%
( 3858
 
5.7%
) 3852
 
5.7%
Other values (16) 11420
16.8%
Han
ValueCountFrequency (%)
內 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68402
68.0%
Hangul 32180
32.0%
Math Operators 74
 
0.1%
CJK 1
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8252
12.1%
7981
11.7%
- 6435
9.4%
4 5909
8.6%
3 5668
8.3%
2 5594
8.2%
5 4837
7.1%
0 4151
 
6.1%
( 3858
 
5.6%
) 3852
 
5.6%
Other values (47) 11865
17.3%
Hangul
ValueCountFrequency (%)
층 3927
 
12.2%
지 1978
 
6.1%
번 1577
 
4.9%
리 1391
 
4.3%
호 1283
 
4.0%
빌 1117
 
3.5%
딩 1080
 
3.4%
동 713
 
2.2%
자 626
 
1.9%
라 607
 
1.9%
Other values (325) 17881
55.6%
Math Operators
ValueCountFrequency (%)
∼ 74
100.0%
CJK
ValueCountFrequency (%)
內 1
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅐ 1
100.0%

MNG_LABOFFICE
Real number (ℝ)

MISSING 

Distinct56
Distinct (%)0.6%
Missing558
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean4353.9503
Minimum2000
Maximum9000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:53:09.199527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2020
Q13000
median5010
Q35170
95-th percentile7110
Maximum9000
Range7000
Interquartile range (IQR)2170

Descriptive statistics

Standard deviation1692.3078
Coefficient of variation (CV)0.38868331
Kurtosis-1.1770569
Mean4353.9503
Median Absolute Deviation (MAD)1210
Skewness-0.046636261
Sum41109999
Variance2863905.7
MonotonicityNot monotonic
2023-12-12T17:53:09.448292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5110 1239
 
12.4%
2060 591
 
5.9%
7000 471
 
4.7%
6000 446
 
4.5%
2040 417
 
4.2%
5160 395
 
4.0%
5000 361
 
3.6%
7210 310
 
3.1%
2020 297
 
3.0%
5180 285
 
2.9%
Other values (46) 4630
46.3%
(Missing) 558
 
5.6%
ValueCountFrequency (%)
2000 209
 
2.1%
2010 126
 
1.3%
2011 86
 
0.9%
2020 297
3.0%
2030 30
 
0.3%
2040 417
4.2%
2050 231
 
2.3%
2060 591
5.9%
2070 115
 
1.1%
2110 24
 
0.2%
ValueCountFrequency (%)
9000 5
 
0.1%
7220 119
 
1.2%
7210 310
3.1%
7120 8
 
0.1%
7110 139
 
1.4%
7001 1
 
< 0.1%
7000 471
4.7%
6310 70
 
0.7%
6220 192
1.9%
6210 33
 
0.3%

MNG_AGENT
Real number (ℝ)

MISSING 

Distinct29
Distinct (%)0.3%
Missing558
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean38.972887
Minimum11
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:53:09.684700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q121
median24
Q361
95-th percentile92
Maximum999
Range988
Interquartile range (IQR)40

Descriptive statistics

Standard deviation28.954283
Coefficient of variation (CV)0.742934
Kurtosis126.71527
Mean38.972887
Median Absolute Deviation (MAD)13
Skewness4.489476
Sum367982
Variance838.3505
MonotonicityNot monotonic
2023-12-12T17:53:09.833103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
11 1527
15.3%
22 1454
14.5%
21 606
 
6.1%
81 595
 
5.9%
41 511
 
5.1%
61 501
 
5.0%
12 462
 
4.6%
24 442
 
4.4%
43 398
 
4.0%
92 395
 
4.0%
Other values (19) 2551
25.5%
(Missing) 558
 
5.6%
ValueCountFrequency (%)
11 1527
15.3%
12 462
 
4.6%
21 606
 
6.1%
22 1454
14.5%
23 350
 
3.5%
24 442
 
4.4%
25 210
 
2.1%
26 393
 
3.9%
31 98
 
1.0%
32 91
 
0.9%
ValueCountFrequency (%)
999 1
 
< 0.1%
99 5
 
0.1%
93 79
 
0.8%
92 395
4.0%
91 109
 
1.1%
85 46
 
0.5%
84 94
 
0.9%
83 157
 
1.6%
82 366
3.7%
81 595
5.9%

PROCESS_STTUS
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
True
9979 
False
 
21
ValueCountFrequency (%)
True 9979
99.8%
False 21
 
0.2%
2023-12-12T17:53:09.978135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

DEL_YN
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
10000 
ValueCountFrequency (%)
False 10000
100.0%
2023-12-12T17:53:10.087415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

FRST_REGISTER_PNTTM
Date

MISSING 

Distinct7163
Distinct (%)75.2%
Missing473
Missing (%)4.7%
Memory size156.2 KiB
Minimum2009-08-17 12:43:00
Maximum2015-02-26 16:40:53
2023-12-12T17:53:10.240401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:10.455794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

LAST_UPDUSR_PNTTM
Date

MISSING 

Distinct2958
Distinct (%)> 99.9%
Missing7041
Missing (%)70.4%
Memory size156.2 KiB
Minimum2012-10-31 17:36:42
Maximum2015-03-27 17:53:20
2023-12-12T17:53:10.655897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:11.636114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

FILE_STRE_COURS1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

STRE_FILE_NM1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

ORIGNL_FILE_NM1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

FILE_STRE_COURS2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

STRE_FILE_NM2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

ORIGNL_FILE_NM2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

FILE_STRE_COURS3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

STRE_FILE_NM3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

ORIGNL_FILE_NM3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

FILE_STRE_COURS4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

STRE_FILE_NM4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

ORIGNL_FILE_NM4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

PYMNT_DCSN_DE
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

RCOGN_NMPR
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

Sample

EDC_YEARUNIQ_IDEDC_DTEDC_TIME_SEDC_TIME_EEDC_SBJECTEDC_CNTLCTRUM_AREDC_PLACEEDC_TYPELCTRUM_ZIPLCTRUM_ADRES1LCTRUM_ADRES2MNG_LABOFFICEMNG_AGENTPROCESS_STTUSDEL_YNFRST_REGISTER_PNTTMLAST_UPDUSR_PNTTMFILE_STRE_COURS1STRE_FILE_NM1ORIGNL_FILE_NM1FILE_STRE_COURS2STRE_FILE_NM2ORIGNL_FILE_NM2FILE_STRE_COURS3STRE_FILE_NM3ORIGNL_FILE_NM3FILE_STRE_COURS4STRE_FILE_NM4ORIGNL_FILE_NM4PYMNT_DCSN_DERCOGN_NMPR
237282013009_20130806211201308068001200건설안전및근로자건강관리5040강의실110<NA>부산 동구 범일2동830-51301081YN2013-08-02 12:03:312013-08-02 15:25:47<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
465442014048_201406191462014061914001800건설안전 및 보건교육5040강의실110<NA>인천 계양구 작전동853-10(5층)501021YN2014-06-16 16:41:18NaT<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
206462013007_20130708241201307089001300건설안전 및 보건위생5040KB건설안전연구원10<NA>경기 오산시 외삼미동53번지 강남빌딩 201호517022YN2013-06-19 10:26:282013-07-05 18:11:55<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
302792013084_20131220260201312207301130건설안전 및 근로자 건강관리5040강서구 재건축 공사 현장20<NA>서울 강서구화곡동204011YN2013-11-26 10:41:59NaT<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
558502014084_20141023860201410238001200건설안전 및 근로자 건강관리5040강의실110<NA>경기 수원시 장안구 영화동392-4 칠공빌딩 3층511022YN2014-09-26 09:44:47NaT<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
233482013053_201308278572013082713001700건설안전및보건교육5040강의실110<NA>부산 사상구 덕포동395-11(2층)302081YN2013-07-29 14:31:05NaT<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
500352014009_20140822978201408228001200건설안전 및 근로자 건강관리5040강의실110<NA>부산 부산진구 범천동886-41300081YN2014-07-22 11:45:14NaT<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
152332013007_201303276172013032712001600건설안전 및 보건위생4030삼성건설 대청댐 비상여수로 현장20<NA>대전 대덕구 미호동643-2번지700041YN2013-03-22 10:00:222013-03-26 16:41:29<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
135342013045_20130225781201302258001200건설업 기초 안전 보건 교육2010김해 진영 중흥 S-클래스 신축공사 현장20<NA>경남 김해시 진영읍진영리 1705313082YN2013-02-21 14:04:29NaT<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
8232012164952012061213301730건설안전 및 근로자 건강관리5040경주감포 국도건설 공사현장 삼성물산 교육장10780822경북 경주시 외동읍 신계리1045번지513023YN2012-06-11 11:20:25NaT<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
EDC_YEARUNIQ_IDEDC_DTEDC_TIME_SEDC_TIME_EEDC_SBJECTEDC_CNTLCTRUM_AREDC_PLACEEDC_TYPELCTRUM_ZIPLCTRUM_ADRES1LCTRUM_ADRES2MNG_LABOFFICEMNG_AGENTPROCESS_STTUSDEL_YNFRST_REGISTER_PNTTMLAST_UPDUSR_PNTTMFILE_STRE_COURS1STRE_FILE_NM1ORIGNL_FILE_NM1FILE_STRE_COURS2STRE_FILE_NM2ORIGNL_FILE_NM2FILE_STRE_COURS3STRE_FILE_NM3ORIGNL_FILE_NM3FILE_STRE_COURS4STRE_FILE_NM4ORIGNL_FILE_NM4PYMNT_DCSN_DERCOGN_NMPR
493822014002_20140710363201407108001200건설안전 및 근로자 건강관리5040강의실110<NA>경기 안양시 동안구 관양동1490-44 3층513023YN2014-07-09 17:27:03NaT<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
522242014012_20140820977201408209001300건설안전 및 근로자 건강관리5040강의실110<NA>서울 구로구 구로동803-4(구로구 도림로 11)206011YN2014-08-14 13:46:382014-08-19 09:43:09<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
263802013038_201309263152013092613001700건설안전 및 보건위생5040강의실1(세종교육장)10<NA>충남 연기군 조치원읍 남리359(목화빌딩 4층)700041YN2013-09-25 15:01:19NaT<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2136201233418201210309001300건설공사 안전 및 근로자 건강관리50<NA>별관 1강의실10<NA>인천광역시 남동구 소래로 688<NA>501021YN2012-09-13 17:54:03NaT<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
428912014054_201405206562014052013301730건설안전 및 근로자 건강관리5040강의실210<NA>경기 화성시 석우동41-5(동탄성심플라자 4층)511022YN2014-04-29 16:37:532014-05-15 16:44:16<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
148572013031_20130425280201304259001300건설공사 안전 및 근로자 건강관리5040강의실410<NA>인천 남동구 만수1동건설기술교육원500021YN2013-03-14 11:35:282013-04-23 13:20:21<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
237952013062_201308079862013080713001700건설안전및근로자건강관리5040강의실210<NA>서울 동작구 대방동341-2(5층)206011YN2013-08-05 09:44:06NaT<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
273812013003_201310109612013101013301730건설안전 및 근로자 건강관리5040강의실110<NA>서울 구로구 구로동33-1 2층206011YN2013-10-08 08:43:522013-10-10 11:19:12<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
471042014023_201407012852014070113001700건설안전 및 근로자 건강관리5040태안화력발전소20<NA>충남 태안군 원북면 방갈리831-3722043YN2014-06-23 15:39:14NaT<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
501702014089_20140730629201407308301230건설안전 및 근로자 건강관리5040강의실110<NA>대구 달서구 본동751400092YN2014-07-25 09:15:03NaT<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>